Gravitational search algorithm: a comprehensive analysis of recent variants

Mittal, Himanshu; Tripathi, Ashish; Pandey, Avinash Chandra; Pal, Raju

doi:10.1007/s11042-020-09831-4

Cited by 69 publications

(23 citation statements)

References 52 publications

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“…Meta-heuristic algorithms are the mathematical models of optimization based on various natural phenomena (Mittal and Saraswat 2018b). Some of the popular meta-heuristic algorithms are differential evolution (DE), genetic algorithm (GA), cuckoo search (CS), particle swarm optimization (PSO), and gravitational search algorithm (GSA) (Mittal et al 2021a). In the literature, many meta-heuristic algorithms have been used for clustering (Kumar and Singh 2018).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A new firefly algorithm-based superpixel clustering method for vehicle segmentation

Tiwari

Saraswat

2022

Soft Comput

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The vehicle segmentation in the images of a crowded and unstructured road traffic, having inconsistent driving patterns and vivid attributes like colour, shapes, and size, is a complex task. For the same, this paper presents a new firefly algorithmbased superpixel clustering method for vehicle segmentation. The proposed method introduces a modified firefly algorithm by incorporating the best solution for enhancing the exploitation behaviour and solution precision. The modified firefly algorithm is further used to obtain the optimal superpixel clusters. The modified firefly algorithm is compared against state-of-the-art meta-heuristic algorithms on IEEE CEC 2015 benchmark problems in terms of mean fitness value, Wilcoxon rank-sum test, convergence behaviour, and box plot. The proposed meta-heuristic algorithm performed superior on more than 80% of the considered benchmark problems. Moreover, the modified firefly algorithm is statistically better on more than 92% of the total problems during Wilcoxon test. Further, the proposed segmentation method is analysed on a traffic dataset to segment the auto-rickshaw. The performance of the proposed method has been compared with kmeans-based superpixel clustering method. The proposed method shows the highest mean value of 0.6242 for Dice coefficient. Both qualitative and quantitative results affirm the efficacy of the proposed method.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A new firefly algorithm-based superpixel clustering method for vehicle segmentation

Tiwari

Saraswat

2022

Soft Comput

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“…In this sub-section, the problems defined by the CEC-2019 Special Season are utilized. Different physics-based methods including the Gravitational Search Algorithm (GSA) 86 and Electromagnetic Field Optimization (EFO) 56 . Furthermore, three recently-developed evolutionary methods including the Farmland Fertility Algorithm (FFA) 35 , African Vultures Optimization Algorithm (AVOA) 37 , and Artificial Gorilla Troops Optimizer (GTO) 42 , are considered for this comparative study.…”

Section: Analyses Based On Competitions On Evolutionary Computation (...mentioning

confidence: 99%

The fusion–fission optimization (FuFiO) algorithm

Nouhi

Darabai

Sareh

et al. 2022

Sci Rep

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Fusion–Fission Optimization (FuFiO) is proposed as a new metaheuristic algorithm that simulates the tendency of nuclei to increase their binding energy and achieve higher levels of stability. In this algorithm, nuclei are divided into two groups, namely stable and unstable. Each nucleus can interact with other nuclei using three different types of nuclear reactions, including fusion, fission, and β-decay. These reactions establish the stabilization process of unstable nuclei through which they gradually turn into stable nuclei. A set of 120 mathematical benchmark test functions are selected to evaluate the performance of the proposed algorithm. The results of the FuFiO algorithm and its related non-parametric statistical tests are compared with those of other metaheuristic algorithms to make a valid judgment. Furthermore, as some highly-complicated problems, the test functions of two recent Competitions on Evolutionary Computation, namely CEC-2017 and CEC-2019, are solved and analyzed. The obtained results show that the FuFiO algorithm is superior to the other metaheuristic algorithms in most of the examined cases.

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“…Every mass exerts a force on another mass under a fixed gravity determining the fitness function. Based on the interaction, the velocity and position of each agent are updated [ 35 ]. The process continues till the termination criteria are met.…”

Section: Application Results and Discussionmentioning

confidence: 99%

Motion-Encoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles

Alanezi

Bouchekara

Shahriar

et al. 2021

Sensors

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In this paper, a new optimization algorithm called motion-encoded electric charged particles optimization (ECPO-ME) is developed to find moving targets using unmanned aerial vehicles (UAV). The algorithm is based on the combination of the ECPO (i.e., the base algorithm) with the ME mechanism. This study is directly applicable to a real-world scenario, for instance the movement of a misplaced animal can be detected and subsequently its location can be transmitted to its caretaker. Using Bayesian theory, finding the location of a moving target is formulated as an optimization problem wherein the objective function is to maximize the probability of detecting the target. In the proposed ECPO-ME algorithm, the search trajectory is encoded as a series of UAV motion paths. These paths evolve in each iteration of the ECPO-ME algorithm. The performance of the algorithm is tested for six different scenarios with different characteristics. A statistical analysis is carried out to compare the results obtained from ECPO-ME with other well-known metaheuristics, widely used for benchmarking studies. The results found show that the ECPO-ME has great potential in finding moving targets, since it outperforms the base algorithm (i.e., ECPO) by as much as 2.16%, 5.26%, 7.17%, 14.72%, 0.79% and 3.38% for the investigated scenarios, respectively.

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Gravitational search algorithm: a comprehensive analysis of recent variants

Cited by 69 publications

References 52 publications

RETRACTED ARTICLE: A new firefly algorithm-based superpixel clustering method for vehicle segmentation

RETRACTED ARTICLE: A new firefly algorithm-based superpixel clustering method for vehicle segmentation

The fusion–fission optimization (FuFiO) algorithm

Motion-Encoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles

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